This page is intended to provide a brief, broad overview of some of my current and past research focus. For more detail, see my publications page.
Computational & Evolutionary Game Theoretic Social/Cultural Modeling
My research on social/cultural modeling focuses on developing computational and evolutionary game theoretic models of human behavior, decision making, culture, and their evolution. Evolutionary computation techniques—and in particular, evolutionary game theory—can be quite useful in providing an understanding of the complex dynamics of human behavior and its evolution in social systems. Behaviors in human social systems are subject to evolutionary selection pressures through cultural adaptation processes such as social learning (e.g. payoff-biased imitation or learning). In this context the fitness of behaviors depends on the abundance of other behaviors in the population, the interaction structure or social network of the population, and other contextual factors.
A primary goal of my research in this area is to enhance understanding of our complex social world by helping to discover relationships between contextual factors and evolutionary behavioral outcomes and dynamics. Specific phenomena I have explored include the evolution of third-party punishment behavior and cross-cultural differences in punishment behavior norms. This research is strongly grounded in social science data and conducted in close collaboration with social scientists. By providing explanatory models of the emergence of different observed behaviors, the evolutionary models developed and studied in my research can establish support for causal relationships among socio-cultural factors and behavioral phenomena that are difficult or impossible to test or infer empirically. An ultimate goal of this research is to create models that can be used to test hypothetical scenarios and create predictive tools that can be of use in a variety of application domains.
Evolution of Decision-Making/Risk Preferences and Culture
My Ph.D. thesis work focused on employing evolutionary game theoretic approaches that combine theoretical analysis and multi-agent systems to generate models of the evolution of human decision-making and culture. This work has shown how a large range of population dynamics (resembling imitation learning) result in state-dependent risk preferences under sequential choice, and how this principle facilitates the evolution of cooperation in classic game-theoretic games where cooperation entails risk.
Evolutionary Graph Theory/Network Science
My research also explores network science related problems, including evolutionary graph theory and data mining in networks for community discovery. In the evolutionary graph theory work we study diffusion, whether in biological networks or to understand how the structure of human social networks influence the dynamics of cultural/social information and behavior.
Computer Music, Music Information Retrieval, & Computational Aesthetics
Prior to coming to UMD, I worked on several projects relating to computer music, music information retrieval, and computational aesthetics with Bill Manaris. This research focuses around applications of machine learning and evolutionary computation techniques to music and aesthetics:
A NSF project on music information retrieval. This work involves the development of a music-search engine prototype to search music libraries based on aesthetic similarity.
A genetic programming music generation system called NEvMusE (Neuro-Evolutionary Music Environment). We conducted several experiments using trained Artificial Neural Networks (ANNs) as fitness critics, i.e. Artificial Art Critics, during the evolutionary music composition process. The ANNs trained on statistical proportions of musical pieces. Samples of evolved music pieces can be found here: NEvMuse